code: def train(self):
      # update the target network of every network that has a target network
      if (
          any(
              [network.has_target for network in self.networks.values()]
          )
          and self._should_update_online_weights_to_target()
      ):
          for network in self.networks.values():
              network.update_target_network(
                  self.ap.algorithm.rate_for_copying_weights_to_target
              )
  
          self.agent_logger.create_signal_value(
              "Update Target Network", 1
          )
      else:
          self.agent_logger.create_signal_value(
              "Update Target Network", 0, overwrite=False
          )
  
      return PolicyOptimizationAgent.train(self)
  
imports: from typing import Union
  import numpy
  from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent
  from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import QHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, NetworkParameters
  from rl_coach.core_types import EnvironmentSteps
  from rl_coach.exploration_policies.e_greedy import EGreedyParameters
  from rl_coach.memories.episodic.single_episode_buffer import SingleEpisodeBufferParameters
  from rl_coach.utils import last_sample
label_desc: Use image node_id 4 for calling the NStepQAgent obj's underlying member method code with example usage: obj.train() and returns: PolicyOptimizationAgent